北挪威大学医院

ABSTRACT:Resistance to B-cell-targeted therapies in immune thrombocytopenia (ITP) has been linked to persistence of autoantibody-producing CD38+ long-lived plasma cells. CD38 antibody daratumumab has been proposed as a potential therapy for ITP. This multicenter, open-label, phase 2 study evaluated safety and efficacy of daratumumab in 21 patients with previously treated ITP. Following a safety run-in, 2 dosing cohorts received 8 and 10 subcutaneous injections of 1800 mg daratumumab weekly, respectively. Primary end points were safety and response (2 consecutive platelet counts ≥50 × 109/L at week 12 for the safety run-in/cohort 1, and at week 16 for cohort 2). At baseline, median platelet count was 17 × 109/L, median number of prior therapies was 4. Most treatment-emergent adverse events were transient grade 1 to 2, most commonly infections (38%). Two patients (4.7%) experienced grade 3 adverse events, 1 infusion-related reaction, and 1 severe acute respiratory syndrome coronavirus 2 infection with acute renal failure. Ten patients (48%) met the primary efficacy end point. Sustained response (2 consecutive platelet counts ≥50 × 109/L at week 24) was achieved in 8 patients (38%), of whom 2 later relapsed. Response and relapse rates did not differ between cohorts. Patient-reported quality of life measured by 36-Item Short-Form Health Survey improved in responding patients. Daratumumab decreased immunoglobulin levels in all patients, and substantially reduced CD38+ cells in peripheral blood and bone marrow. There was no significant difference in antiplatelet antibodies between responders and nonresponders. This study confirms CD38 as an important target in ITP. This trial was registered at clinicaltrials.gov as #NCT04703621, and at the European Clinical Trial Register (EudraCT #2019-004683-22).

Importance:Artificial intelligence (AI) models are emerging as rapid, low-cost tools for predicting targetable genomic alterations directly from routine pathology slides. Although these approaches could accelerate treatment decisions in lung cancer, little is known about whether their performance is consistent across diverse patient populations and tissue contexts. Objective:To evaluate the performance and generalizability of 2 open-source AI pathology models for predicting EGFR mutation status in lung adenocarcinoma (LUAD) across independent cohorts and ancestral subgroups. Design, Setting, and Participants:This cohort study included patients with LUAD from 2 cohorts: Dana-Farber Cancer Institute (DFCI) from June 2013 to November 2023, and a European-based trial (TNM-I) from August 2016 to February 2022. All patients had paired next-generation sequencing data and hematoxylin-eosin-stained whole-slide images. In the DFCI cohort, genetic ancestry was inferred using germline genotype data. Data analyses were performed from July 2025 to September 2025. Main Outcomes:The primary outcome was model performance for predicting EGFR mutation status, measured as the area under the receiver operating characteristic curve (AUC), evaluated overall and across ancestry subgroups and sample types. Results:Overall, 2098 patients with LUAD were included (mean [SD] age, 66.6 [10.3] years; 1315 female individuals [63%] and 783 male individuals [37%]). In the DFCI cohort (n = 1759; 54 African, 101 American, 95 Asian, 1465 European), EGFR mutations were detected in 432 patients (25%). One AI-pathology model achieved an AUC of 0.83 (95% CI, 0.81-0.85) compared with 0.68 (95% CI, 0.65-0.70) for the other model. In the TNM-I cohort (n = 339), EGFR mutations were detected in 50 patients (15%), with AUCs of 0.81 (95% CI, 0.74-0.88) and 0.75 (95% CI, 0.68-0.83), respectively. In ancestry-stratified analyses of the DFCI cohort, AUCs for the higher-performing model were 0.84 (95% CI, 0.81-0.86) in patients of European ancestry, 0.85 (95% CI, 0.72-0.94) in African ancestry, and 0.68 (95% CI, 0.55-0.78) in Asian ancestry. In sample type analyses, performance declined in pleural (AUC, 0.66; 95% CI, 0.56-0.76) compared with lung specimens (AUC, 0.86; 95% CI, 0.83-0.88). AI-guided triage analyses showed a potential 57% reduction in rapid EGFR testing, while maintaining sensitivity of 0.84 and specificity of 0.99. Conclusions:This cohort study found that AI-based pathology tools may serve as preliminary adjuncts for EGFR prediction in lung cancer, though performance differences by ancestry warrant careful interpretation.

BACKGROUND:Biomarkers could improve risk stratification in patients with acute ST-segment elevation myocardial infarction (STEMI), beyond left ventricular ejection fraction (LVEF). Our study evaluated the association between N-terminal pro-B-type natriuretic peptide (NT-proBNP), C reactive protein (CRP) and mortality in a cohort of patients with acute STEMI. METHODS:This prospective, observational cohort study included patients with reperfused acute STEMI admitted to a tertiary cardiovascular disease centre between July 2020 and October 2023. All patients underwent NT-proBNP and CRP testing. The association between NT-proBNP, CRP and all-cause mortality was evaluated in relation to predischarge LVEF. RESULTS:The cohort included 566 patients with a mean age of 63 years. After a median follow-up of 39 months, postdischarge all-cause mortality reached 13.4%. NT-proBNP was associated with mortality irrespective of LVEF (HR 2.34 per SD increment in log NT-proBNP; p<0.001 at LVEF <50% and HR 2.36; p=0.004 at LVEF ≥50%), but the association between CRP and mortality was significant only in patients with LVEF <50% (HR 1.55, p=0.003). Across the cohort, NT-proBNP remained associated with death after adjustment for age, sex, diabetes, baseline high-sensitivity cardiac troponin T (hs-cTnT), CRP, final Thrombolysis in myocardial infarction (TIMI) flow grade and reduced LVEF (HR 1.45, p=0.03). In patients with preserved LVEF, routine NT-proBNP testing (area under the curve (AUC) 0.753 (0.642-0.863), p<0.001) improved risk stratification compared with isolated LVEF assessment (AUC 0.592 (0.453-0.730), p=0.18). CONCLUSIONS:In a cohort of stabilised acute STEMI survivors, NT-proBNP was associated with all-cause mid-term mortality independent of hs-cTnT and LVEF. The association between CRP and mortality was significant only in patients with LVEF <50%.

BackgroundGaza has faced numerous military attacks that resulted in mass casualty incidents (MCIs). The ongoing genocide in Gaza has destroyed much of the health system, including killing and injuring of hundreds of health care workers (HCWs). Current thinking on the health system reconstruction lacks empirical data and local HCWs' perspectives. The study analyses locally driven innovations and lessons learned by HCWs who responded to MCIs between 2018 and 2021 to guide current and future planning of the reconstruction of the health system in Gaza.MethodsThis was a qualitative study using online and face-to-face interviews with HCWs who responded to the Great March of Return and the 2021 Israeli military attacks. Transcripts and extensive notes from the interviews were recorded and analyzed on NVivo using thematic content analysis. We used the health system building blocks as themes for deductive analysis with a seventh place-based theme (Gaza-specific) to account for the context of Gaza and the MCIs.ResultsProblems faced by HCWs mostly related to the nature and complexity of traumatic injuries, shortages in HCWs, particularly specialist doctors, poor coordination among actors, duplication of services, and shortages of supplies and equipment. Locally driven innovations and solutions included establishing new services centers, opening and expanding training programs, starting new coordination bodies, and task shifting of staff and facilities. Lessons learned included strengthening training and employment opportunities for staff, enhancing emergency preparedness and capacities, maintaining coordination bodies, enhancing community engagement and strengthening the governance of the Ministry of Health.ConclusionReconstruction of Gaza's health system needs to be grounded in its political context and in the experiences of HCWs who have worked in and managed the system. Locally driven solutions and lessons learned can ensure that reconstruction serves as a vehicle for self-determination and sovereignty, rather than entrenching dependency.

Background:Lumbar spinal stenosis is a leading indication for spine surgery, but outcomes are heterogeneous. We aimed to develop and externally validate prediction models for 12-month disability and pain to inform shared decision-making. Methods:This registry-based multicentre cohort study used data from three national spine registries of patients (≥16 years) undergoing elective lumbar spinal stenosis surgery. Data from the Norwegian Registry for Spine Surgery (NORspine, 2007-2023) were used for model development and internal-external cross-validation (IECV). External validation was carried out in the Swedish Registry (SweSpine, 2016-2022) and Danish Registry (DaneSpine, 2009-2022) with data collected by the Spine Centre of Southern Denmark. The primary outcome was the Oswestry Disability Index (ODI) at 12 months, modelled as a continuous and binary measure (acceptable symptom state). Secondary outcomes were Numeric Rating Scale (NRS) back and leg pain at 12 months. Logistic regression, linear regression, and XGBoost models were applied with 16 predictors. Missing data were handled using multiple imputation. Performance was assessed by calibration, mean absolute error (MAE), adjusted R2, and C-statistics. This study is registered with Open Science Framework (https://osf.io/qz27b/). Findings:The development cohort included 31,908 patients (52.4% female, 47.6% male). The external validation cohorts included 30,700 from SweSpine (52.8% female, 47.2% male) and 4063 from DaneSpine (54.6% female, 45.4% male). Twelve-month outcome completeness was 77% in the development cohort and ranged from 66% to 80% across the external validation cohorts. For ODI, linear regression achieved a pooled MAE of 12.4 (95% CI 11.8-13.1) after IECV, and 13.3 (95% CI 13.2-13.4) and 12.3 (95% CI 12.0-12.7) at external validation. Adjusted R2 values ranged from 0.26 to 0.33. Calibration was acceptable, with slopes near 1 and calibration-in-the-large ranging from -0.47 after IECV to 1.28-1.54 at external validation, indicating minor systematic underprediction. The binary ODI model achieved C-statistics of 0.75 (95% CI 0.74-0.76) after IECV, and 0.78 (95% CI 0.78-0.79) and 0.76 (95% CI 0.74-0.77) at external validation. Pain models showed lower performance (MAE 2.2-2.6; C-statistics 0.64-0.73). XGBoost yielded similar results. Interpretation:Models predicting disability and pain were well calibrated and generalisable across Scandinavian countries, with the best overall performance for disability. These findings provide a foundation for prospective evaluation in future studies to determine the impact on decision-making and patient outcomes in clinical practice. Funding:Research Council of Norway.